op {
  graph_op_name: "TensorScatterAdd"
  in_arg {
    name: "tensor"
    description: <<END
Tensor to copy/update.
END
  }
  in_arg {
    name: "indices"
    description: <<END
Index tensor.
END
  }
  in_arg {
    name: "updates"
    description: <<END
Updates to scatter into output.
END
  }
  out_arg {
    name: "output"
    description: <<END
A new tensor copied from tensor and updates added according to the indices.
END
  }
  summary: "Adds sparse `updates` to an existing tensor according to `indices`."
  description: <<END
This operation creates a new tensor by adding sparse `updates` to the passed
in `tensor`.
This operation is very similar to `tf.compat.v1.scatter_nd_add`, except that the
updates are added onto an existing tensor (as opposed to a variable). If the
memory for the existing tensor cannot be re-used, a copy is made and updated.

`indices` is an integer tensor containing indices into a new tensor of shape
`tensor.shape`.  The last dimension of `indices` can be at most the rank of
`tensor.shape`:

```
indices.shape[-1] <= tensor.shape.rank
```

The last dimension of `indices` corresponds to indices into elements
(if `indices.shape[-1] = tensor.shape.rank`) or slices
(if `indices.shape[-1] < tensor.shape.rank`) along dimension
`indices.shape[-1]` of `tensor.shape`.  `updates` is a tensor with shape

```
indices.shape[:-1] + tensor.shape[indices.shape[-1]:]
```

The simplest form of `tensor_scatter_nd_add` is to add individual elements to a
tensor by index. For example, say we want to add 4 elements in a rank-1
tensor with 8 elements.

In Python, this scatter add operation would look like this:

>>> indices = tf.constant([[4], [3], [1], [7]])
>>> updates = tf.constant([9, 10, 11, 12])
>>> tensor = tf.ones([8], dtype=tf.int32)
>>> updated = tf.tensor_scatter_nd_add(tensor, indices, updates)
>>> updated
<tf.Tensor: shape=(8,), dtype=int32,
numpy=array([ 1, 12,  1, 11, 10,  1,  1, 13], dtype=int32)>

We can also, insert entire slices of a higher rank tensor all at once. For
example, if we wanted to insert two slices in the first dimension of a
rank-3 tensor with two matrices of new values.

In Python, this scatter add operation would look like this:

>>> indices = tf.constant([[0], [2]])
>>> updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],
...                         [7, 7, 7, 7], [8, 8, 8, 8]],
...                        [[5, 5, 5, 5], [6, 6, 6, 6],
...                         [7, 7, 7, 7], [8, 8, 8, 8]]])
>>> tensor = tf.ones([4, 4, 4],dtype=tf.int32)
>>> updated = tf.tensor_scatter_nd_add(tensor, indices, updates)
>>> updated
<tf.Tensor: shape=(4, 4, 4), dtype=int32,
numpy=array([[[6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8], [9, 9, 9, 9]],
             [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
             [[6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8], [9, 9, 9, 9]],
             [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]], dtype=int32)>

Note: on CPU, if an out of bound index is found, an error is returned.
On GPU, if an out of bound index is found, the index is ignored.
END
}
